Here we show scatterplots comparing expression levels for all genes between the different samples, for i) all controls, ii) all treatment samples and iii) for all samples together.
These plots will only be produced when the total number of samples to compare within a group is less than or equal to 10.
BROWN: higher correlation; YELLOW: lower
This is a PCA plot of the count values normalized following the default method and then they are scaled:
Graphical representation of PCA dimensions. The bars represent the percentage of total variance that summarize each dimension. The line measures the percentage of total variance accumulated in previous dimensions. The color distinguishes between significan or no significant dimensions. Only significant dimensions will be considered in the following plots.
The eigenvector contains the weights of each gene for the PC. Here are represented the distributions of the weights of each eigenvector. The vertical lines represent the quantiles.
This plot compare the position of samples and their distribution in the significant dimensions. The color differenciate between the control (red) and treat (blue) samples.
Fisher’s exact test is computed between clusters and experimental treats. Fisher’s exact test P values and FDR are showed.
These boxplots show the distributions of count data before and after normalization (shown for normalization method default):
Sample rank is the position a sample holds after sorting by total counts
Samples are ranked by total expressed genes. Union of expressed genes represents the cumulative total expressed genes (sum of all genes expressed in any sample up to current sample, expected to increase with sample rank). Intersection of expressed genes represents the cumulative intersection of expressed genes (sum of genes expressed in all samples up to current sample, expected to decrease with sample rank).
This plot represents the mean counts distribution per gene, classified by filters
Variance of gene counts across samples are represented. Genes with lower variance than selected threshold (dashed grey line) were filtered out.
All counts were normalizated by default (see options below) algorithm. This count were scaled by log10 and plotted in a heatmap.
| M_19254 | M_19256 | M_19257 | M_20268 | M_20282 | M_20283 | M_20284 | M_20281 | M_20279 | M_20262 | M_20280 | M_20270 | M_20272 | M_20273 | M_20296 | M_20289 | M_20275 | M_20276 | M_20301 | M_20293 | M_20286 | M_20292 | M_20290 | M_20291 | M_20294 | M_20295 | M_20298 | M_20299 | M_20302 | M_20303 | M_20304 | M_20312 | M_20307 | M_20308 | M_20309 | M_20310 | M_20311 | M_20319 | M_20318 | M_20328 | M_20313 | M_20315 | M_20316 | M_20317 | M_20322 | M_20327 | M_20325 | M_20326 | M_20324 | M_20329 | M_20331 | M_1273 | M_1280 | M_1284 | M_1285 | M_1286 | M_1287 | M_1290 | M_1292 | M_1274 | M_1295 | M_1296 | M_1298 | M_1299 | M_12150 | M_12151 | M_12152 | M_12153 | M_12154 | M_12155 | M_12156 | M_13158 | M_13160 | M_13162 | M_13163 | M_13164 | M_13165 | M_13168 | M_16173 | M_16184 | M_16170 | M_16172 | M_16181 | M_16182 | M_16171 | M_16174 | M_16175 | M_16180 | M_16178 | M_19255 | M_16186 | M_16176 | M_16177 | M_16188 | M_16189 | M_16179 | M_16187 | M_16195 | M_16183 | M_16185 | M_16196 | M_16200 | M_16190 | M_16191 | M_16194 | M_16192 | M_17204 | M_16197 | M_16198 | M_16199 | M_16201 | M_17203 | M_17208 | M_17214 | M_17205 | M_17206 | M_17209 | M_17207 | M_17210 | M_17211 | M_17212 | M_17216 | M_17213 | M_17215 | M_19223 | M_19218 | M_19219 | M_19220 | M_19222 | M_19229 | M_19221 | M_19245 | M_19230 | M_19224 | M_19232 | M_19227 | M_19228 | M_19233 | M_19234 | M_19240 | M_19261 | M_19235 | M_19249 | M_19248 | M_19236 | M_19238 | M_19251 | M_19252 | M_19237 | M_19253 | M_19246 | M_19247 | M_20267 | M_20265 | M_20264 | M_19260 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000274012.1 | 6.878 | 3.932 | 3.825 | 3.848 | 6.135 | 5.342 | 6.342 | 5.491 | 5.454 | 10.132 | 5.588 | 6.666 | 5.520 | 7.013 | 9.263 | 5.777 | 10.510 | 7.662 | 10.495 | 7.571 | 6.800 | 6.321 | 6.408 | 7.025 | 6.735 | 6.834 | 8.957 | 9.044 | 5.904 | 7.805 | 10.442 | 7.127 | 8.814 | 6.953 | 8.204 | 7.097 | 5.204 | 5.650 | 5.739 | 7.633 | 7.085 | 6.187 | 5.533 | 4.761 | 6.765 | 6.619 | 6.928 | 8.186 | 5.581 | 7.871 | 7.720 | 6.752 | 7.150 | 8.169 | 5.610 | 7.955 | 8.417 | 6.233 | 9.142 | 7.884 | 8.840 | 5.824 | 4.374 | 7.298 | 4.741 | 6.361 | 4.784 | 6.805 | 5.405 | 6.073 | 5.776 | 8.347 | 6.818 | 9.328 | 7.851 | 8.075 | 8.308 | 5.963 | 6.097 | 7.206 | 5.416 | 7.223 | 7.745 | 7.439 | 7.411 | 7.229 | 7.043 | 8.241 | 7.928 | 7.720 | 6.924 | 7.833 | 7.805 | 4.415 | 10.227 | 7.705 | 6.728 | 9.119 | 9.155 | 8.705 | 7.370 | 7.204 | 6.911 | 7.317 | 9.526 | 5.577 | 5.269 | 13.838 | 6.578 | 7.102 | 6.627 | 5.402 | 9.061 | 7.999 | 5.839 | 7.713 | 7.801 | 9.221 | 8.846 | 9.086 | 9.556 | 6.125 | 8.132 | 5.850 | 11.158 | 7.597 | 8.778 | 7.478 | 6.224 | 5.359 | 9.484 | 5.315 | 6.034 | 7.023 | 6.911 | 8.029 | 7.167 | 6.441 | 7.263 | 6.567 | 7.213 | 4.749 | 5.652 | 5.448 | 5.434 | 7.643 | 7.475 | 5.551 | 5.345 | 4.849 | 6.317 | 6.183 | 7.675 | 7.224 | 9.346 | 8.154 |
| ENSG00000276168.1 | 4.705 | 3.269 | 3.573 | 4.508 | 5.140 | 4.848 | 5.758 | 4.871 | 5.031 | 8.814 | 5.582 | 6.931 | 6.090 | 6.854 | 7.929 | 5.754 | 9.958 | 6.841 | 8.771 | 7.399 | 5.271 | 6.161 | 5.792 | 6.286 | 6.728 | 6.343 | 8.351 | 6.929 | 5.700 | 7.154 | 9.599 | 6.025 | 7.145 | 6.022 | 6.173 | 5.418 | 4.210 | 5.384 | 4.382 | 6.510 | 5.789 | 4.217 | 3.270 | 3.222 | 5.121 | 5.695 | 6.649 | 5.875 | 3.863 | 7.584 | 6.161 | 6.177 | 5.559 | 3.616 | 5.329 | 6.291 | 6.929 | 4.901 | 5.901 | 6.578 | 7.230 | 5.351 | 4.598 | 6.793 | 4.502 | 5.351 | 4.620 | 6.470 | 4.086 | 4.740 | 5.234 | 7.482 | 6.668 | 8.155 | 5.016 | 6.437 | 7.007 | 4.707 | 4.231 | 5.760 | 4.680 | 4.922 | 6.865 | 4.754 | 5.780 | 6.372 | 4.947 | 5.767 | 6.378 | 5.786 | 6.819 | 6.110 | 7.035 | 5.014 | 8.076 | 6.383 | 5.657 | 7.002 | 6.713 | 8.074 | 6.772 | 6.974 | 5.265 | 5.949 | 6.396 | 3.781 | 4.733 | 12.153 | 5.037 | 5.671 | 2.716 | 5.000 | 8.082 | 6.380 | 4.594 | 6.253 | 7.075 | 7.963 | 7.918 | 6.419 | 7.978 | 5.073 | 7.426 | 4.473 | 9.653 | 6.206 | 7.504 | 6.426 | 4.999 | 4.705 | 7.318 | 4.692 | 5.063 | 5.245 | 4.469 | 7.052 | 5.568 | 4.720 | 5.807 | 5.194 | 4.476 | 4.129 | 5.356 | 4.329 | 4.005 | 6.450 | 4.012 | 4.508 | 5.153 | 4.204 | 4.555 | 5.314 | 6.045 | 5.140 | 7.586 | 6.548 |
| ENSG00000283293.1 | 1.776 | 0.969 | 1.057 | 1.533 | 1.656 | 1.911 | 1.781 | 1.888 | 2.166 | 2.201 | 1.871 | 2.069 | 1.792 | 2.355 | 2.176 | 2.137 | 1.628 | 1.946 | 2.239 | 1.697 | 2.053 | 1.923 | 2.152 | 2.438 | 1.885 | 2.130 | 2.082 | 2.610 | 1.833 | 2.051 | 2.434 | 1.476 | 2.743 | 1.367 | 2.047 | 1.838 | 1.423 | 1.835 | 1.692 | 2.186 | 1.480 | 1.569 | 2.630 | 2.157 | 1.768 | 1.724 | 1.791 | 1.769 | 1.787 | 2.027 | 2.265 | 1.819 | 2.264 | 1.835 | 1.904 | 2.073 | 1.912 | 1.837 | 2.327 | 1.742 | 2.019 | 1.702 | 1.771 | 2.067 | 1.642 | 1.858 | 1.444 | 1.454 | 1.250 | 1.949 | 1.831 | 2.252 | 1.838 | 1.947 | 2.430 | 1.975 | 2.104 | 1.566 | 2.202 | 1.845 | 1.591 | 2.083 | 2.021 | 2.307 | 1.685 | 1.917 | 1.401 | 2.239 | 2.043 | 1.553 | 1.791 | 1.940 | 2.158 | 1.357 | 2.362 | 1.949 | 1.554 | 1.684 | 2.490 | 1.966 | 1.779 | 2.242 | 1.768 | 1.393 | 2.039 | 1.742 | 1.150 | 2.123 | 1.639 | 1.307 | 1.680 | 1.131 | 2.327 | 1.921 | 1.395 | 1.908 | 1.775 | 1.928 | 2.105 | 2.221 | 2.136 | 1.565 | 2.076 | 1.722 | 2.626 | 2.214 | 2.354 | 1.612 | 1.494 | 1.172 | 2.588 | 1.351 | 1.568 | 2.233 | 1.764 | 2.058 | 1.700 | 1.347 | 1.275 | 1.644 | 1.707 | 1.304 | 1.313 | 1.399 | 1.661 | 2.194 | 1.321 | 1.406 | 1.364 | 1.134 | 1.408 | 1.424 | 1.748 | 1.544 | 1.894 | 1.730 |
| ENSG00000251562.9 | 1.656 | 1.311 | 1.474 | 0.946 | 1.224 | 1.150 | 1.684 | 1.362 | 1.170 | 1.041 | 1.227 | 1.051 | 1.121 | 1.271 | 1.202 | 0.976 | 1.069 | 1.412 | 1.180 | 1.221 | 1.297 | 1.351 | 1.018 | 1.153 | 1.509 | 1.258 | 1.066 | 1.218 | 1.504 | 1.247 | 0.817 | 1.337 | 1.099 | 1.403 | 1.223 | 1.794 | 1.891 | 1.496 | 1.574 | 1.482 | 1.583 | 1.422 | 1.005 | 1.330 | 1.457 | 1.239 | 1.464 | 1.653 | 1.666 | 1.245 | 1.163 | 1.565 | 1.407 | 1.421 | 1.554 | 1.433 | 1.454 | 1.381 | 1.284 | 1.290 | 1.259 | 1.287 | 0.389 | 1.454 | 1.290 | 1.289 | 1.699 | 1.580 | 1.564 | 1.171 | 1.654 | 1.188 | 1.173 | 1.279 | 1.335 | 1.212 | 1.238 | 1.556 | 1.695 | 1.216 | 1.626 | 1.656 | 1.213 | 1.632 | 1.368 | 1.382 | 1.591 | 1.203 | 1.265 | 1.428 | 1.170 | 1.414 | 1.125 | 1.567 | 1.155 | 1.280 | 1.590 | 1.226 | 1.170 | 0.980 | 1.336 | 1.037 | 1.189 | 1.475 | 1.066 | 1.772 | 1.513 | 0.091 | 1.732 | 0.883 | 1.336 | 1.095 | 1.061 | 1.263 | 1.273 | 1.158 | 1.285 | 1.256 | 1.210 | 1.264 | 1.182 | 1.667 | 1.107 | 1.479 | 0.854 | 1.248 | 0.965 | 1.225 | 1.424 | 1.373 | 1.252 | 1.277 | 1.422 | 1.810 | 1.213 | 1.247 | 1.651 | 1.318 | 1.149 | 1.144 | 1.676 | 1.123 | 1.550 | 1.306 | 1.487 | 1.316 | 0.844 | 1.490 | 1.150 | 1.349 | 1.241 | 1.223 | 1.255 | 1.615 | 1.167 | 1.242 |
| ENSG00000124942.14 | 1.066 | 1.622 | 1.310 | 0.788 | 1.058 | 0.968 | 1.091 | 1.273 | 1.279 | 1.110 | 1.024 | 0.912 | 1.049 | 1.014 | 1.176 | 1.047 | 0.911 | 1.200 | 1.130 | 0.921 | 1.014 | 1.080 | 0.956 | 1.088 | 1.110 | 1.182 | 0.981 | 0.889 | 1.047 | 1.092 | 0.803 | 1.030 | 0.870 | 1.051 | 1.274 | 1.374 | 1.533 | 1.211 | 1.171 | 1.117 | 1.138 | 1.238 | 0.961 | 1.330 | 1.137 | 1.159 | 1.171 | 1.222 | 1.362 | 0.898 | 1.286 | 1.015 | 0.972 | 1.365 | 1.261 | 1.070 | 0.950 | 1.025 | 1.179 | 0.975 | 1.093 | 1.030 | 0.928 | 1.011 | 1.150 | 1.183 | 1.153 | 1.246 | 1.332 | 1.428 | 1.246 | 0.889 | 1.049 | 0.964 | 0.888 | 1.293 | 1.106 | 1.316 | 0.928 | 1.110 | 1.329 | 1.313 | 1.203 | 0.943 | 1.423 | 1.347 | 1.191 | 1.356 | 1.169 | 1.308 | 1.030 | 1.506 | 1.093 | 1.008 | 0.963 | 1.169 | 1.200 | 1.297 | 1.156 | 1.116 | 1.284 | 1.262 | 1.624 | 1.134 | 1.200 | 1.167 | 1.123 | 0.443 | 1.083 | 1.212 | 1.384 | 1.473 | 1.080 | 1.029 | 1.390 | 1.205 | 0.960 | 0.895 | 0.875 | 1.009 | 1.162 | 1.424 | 1.095 | 1.197 | 1.027 | 1.297 | 1.184 | 1.185 | 1.126 | 1.285 | 0.819 | 1.393 | 1.411 | 1.317 | 1.586 | 1.327 | 1.070 | 1.581 | 1.193 | 1.501 | 1.490 | 1.208 | 1.216 | 1.492 | 1.198 | 1.009 | 1.093 | 1.311 | 0.998 | 1.345 | 1.248 | 1.138 | 1.125 | 1.313 | 0.923 | 1.223 |
| Sample Names: |
|---|
| M_19254 |
| M_19256 |
| M_19257 |
| M_20268 |
| M_20282 |
| M_20283 |
| M_20284 |
| M_20281 |
| M_20279 |
| M_20262 |
| M_20280 |
| M_20270 |
| M_20272 |
| M_20273 |
| M_20296 |
| M_20289 |
| M_20275 |
| M_20276 |
| M_20301 |
| M_20293 |
| M_20286 |
| M_20292 |
| M_20290 |
| M_20291 |
| M_20294 |
| M_20295 |
| M_20298 |
| M_20299 |
| M_20302 |
| M_20303 |
| M_20304 |
| M_20312 |
| M_20307 |
| M_20308 |
| M_20309 |
| M_20310 |
| M_20311 |
| M_20319 |
| M_20318 |
| M_20328 |
| M_20313 |
| M_20315 |
| M_20316 |
| M_20317 |
| M_20322 |
| M_20327 |
| M_20325 |
| M_20326 |
| M_20324 |
| M_20329 |
| M_20331 |
| M_1273 |
| M_1280 |
| M_1284 |
| M_1285 |
| M_1286 |
| M_1287 |
| M_1290 |
| M_1292 |
| M_1274 |
| M_1295 |
| M_1296 |
| M_1298 |
| M_1299 |
| M_12150 |
| M_12151 |
| M_12152 |
| M_12153 |
| M_12154 |
| M_12155 |
| M_12156 |
| M_13158 |
| M_13160 |
| M_13162 |
| M_13163 |
| M_13164 |
| M_13165 |
| M_13168 |
| Sample Names: |
|---|
| M_16173 |
| M_16184 |
| M_16170 |
| M_16172 |
| M_16181 |
| M_16182 |
| M_16171 |
| M_16174 |
| M_16175 |
| M_16180 |
| M_16178 |
| M_19255 |
| M_16186 |
| M_16176 |
| M_16177 |
| M_16188 |
| M_16189 |
| M_16179 |
| M_16187 |
| M_16195 |
| M_16183 |
| M_16185 |
| M_16196 |
| M_16200 |
| M_16190 |
| M_16191 |
| M_16194 |
| M_16192 |
| M_17204 |
| M_16197 |
| M_16198 |
| M_16199 |
| M_16201 |
| M_17203 |
| M_17208 |
| M_17214 |
| M_17205 |
| M_17206 |
| M_17209 |
| M_17207 |
| M_17210 |
| M_17211 |
| M_17212 |
| M_17216 |
| M_17213 |
| M_17215 |
| M_19223 |
| M_19218 |
| M_19219 |
| M_19220 |
| M_19222 |
| M_19229 |
| M_19221 |
| M_19245 |
| M_19230 |
| M_19224 |
| M_19232 |
| M_19227 |
| M_19228 |
| M_19233 |
| M_19234 |
| M_19240 |
| M_19261 |
| M_19235 |
| M_19249 |
| M_19248 |
| M_19236 |
| M_19238 |
| M_19251 |
| M_19252 |
| M_19237 |
| M_19253 |
| M_19246 |
| M_19247 |
| M_20267 |
| M_20265 |
| M_20264 |
| M_19260 |
Note: A positive log fold change shows higher expression in the treatment group; a negative log fold change represents higher expression in the control group.
DEgenes Hunter uses multiple DE detection packages to analyse all genes in the input count table and labels them accordingly:
minpack_common argument.This barplot shows the total number of genes passing each stage of analysis - from the total number of genes in the input table of counts, to the genes surviving the expression filter, to the genes detected as DE by one package, to the genes detected by at least 4 packages.
This is the Venn Diagram of all possible DE genes (DEGs) according to at least on of the DE detection packages employed:
Benchmark of false positive calling:
Boxplot of FDR values among all genes with an FDR <= 0.05 in at least one DE detection package
The red horizontal line represents the chosen FDR threshold of 0.05. The black lines represent other values.
This is a PCA plot of the count values normalized following the default method and then they are scaled:
Graphical representation of PCA dimensions. The bars represent the percentage of total variance that summarize each dimension. The line measures the percentage of total variance accumulated in previous dimensions. The color distinguishes between significan or no significant dimensions. Only significant dimensions will be considered in the following plots.
The eigenvector contains the weights of each gene for the PC. Here are represented the distributions of the weights of each eigenvector. The vertical lines represent the quantiles.
This plot compare the position of samples and their distribution in the significant dimensions. The color differenciate between the control (red) and treat (blue) samples.
Fisher’s exact test is computed between clusters and experimental treats. Fisher’s exact test P values and FDR are showed.
The complete results of the DEgenes Hunter differential expression analysis can be found in the “hunter_results_table.txt” file in the Common_results folder
Various plots specific to each package are shown below:
The effective library size is the factor used by DESeq2 normalization algorithm for each sample. The effective library size must be dependent of raw library size.
This plot compares the effective library size with raw library size
The effective library size is the factor used by DESeq2 normalization algorithm for each sample. The effective library size must be dependent of raw library size.
This is the MA plot from DESeq2 package:
In DESeq2, the MA-plot (log ratio versus abundance) shows the log2 fold changes are attributable to a given variable over the mean of normalized counts. Points will be colored red if the adjusted Pvalue is less than 0.1. Points which fall out of the window are plotted as open triangles pointing either up or down.
A table containing the DESeq2 DEGs is provided: in Results_DESeq2/DEgenes_DESEq2.txt
A table containing the DESeq2 normalized counts is provided in Results_DESeq2/Normalized_counts_DESEq2.txt
Counts of prevalent DEGs were normalizated by DESeq2 algorithm. This count were scaled by log10 and plotted in a heatmap.
This is an advanced section in order to compare the output of the packages used to perform data analysis. The data shown here does not necessarilly have any biological implication.
Distributions of p-values, unadjusted and adjusted for multiple testing (FDR)
Correlations of adjusted p-values, adjusted for multiple testing (FDR) and for log Fold Change.
First column contains the option names; second column contains the given values for each option in this run.
| opt | |
|---|---|
| input_file | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/data/pseudocounts_table.txt |
| pseudocounts | TRUE |
| reads | 2 |
| count_var_quantile | 0 |
| minlibraries | 2 |
| filter_type | separate |
| output_files | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/reboot/results |
| p_val_cutoff | 0.05 |
| lfc | 1 |
| modules | D |
| minpack_common | 4 |
| target_file | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/data/parse_outbrat/parsed_files/experiment_design.txt |
| model_variables | |
| numerics_as_factors | FALSE |
| string_factors | Etnia,Sex,AgeHigher50,Smoker,Hypertension,Dyslipidemia,Diabetes,Obesity,Antihypertensives_HPM,DM2,AntiDM,Quartils.Temp,Quartils.MinTemp,Quartil.MaxTemp,Healthy,FIB4.stage,FIB4.class,FIB4.Adv.Fib |
| numeric_factors | Age,Weight,Height,BMI,Waist,Hip,whr,sbp,dbp,AntiHPN_T1,AntiHPN_T2,AntiHPN_T3,AntiHPN_T4,Glucose,HbA1c,AntiDM_T1,AntiDM_T2,AntiDM_T3,AntiDM_T4,AntiDM_T5,Cholesterol,HDLc,LDLc,Triglycerides,Creatinine,GOT,GPT,ultraPCR,Ferritin,Hemoglobin,Hematocrit,Hematies,Platelets,MinTemp,MaxTemp,AverageTemp,MinTempBefore,MaxTempBefore,AverageTempBefore,MeanMinTemp,MeanMaxTemp,MeanAvgTemp,As,AsBefore,BenzoPireno,BenzoPirenoBefore,C6H6,C6H6Before,CO,CObefore,NO,NObefore,NO2,NO2before,NOX,NOXbefore,O3,O3before,PM2.5,PM2.5before,PM10,PM10before,HoresSol_Suma,HoresSol_Mitja,FIB4.score |
| WGCNA_memory | 5000 |
| WGCNA_norm_method | DESeq2 |
| WGCNA_deepsplit | 2 |
| WGCNA_min_genes_cluster | 20 |
| WGCNA_detectcutHeight | 0.995 |
| WGCNA_mergecutHeight | 0.25 |
| WGCNA_all | FALSE |
| WGCNA_blockwiseNetworkType | signed |
| WGCNA_blockwiseTOMType | signed |
| WGCNA_minCoreKME | 0.7 |
| WGCNA_minKMEtoStay | 0.5 |
| WGCNA_corType | pearson |
| multifactorial | |
| help | FALSE |